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%
% File:               marginal_design.m
%
% Authors:            Sergio Ascencio and Miguel Rueda
%
% Description:        Creates datasets with info for simulated probabilities.
% Language:           MATLAB R2013b (8.2.0.701) 64 Bit

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function [mean_X_PRI,mean_X_PAN]=marginal_design(Y,X,beta,var,range_var,pri_pan)

%varsx={'l_d_RC_others' 'l_margin' 'l_mesas' 'l_school_i' 'l_diff_PRI_PAN' 'l_lpop' 'l_lpop_sq' 'l_margin_sq' 'l_diff_PRI_PAN_sq' 'l_mesas_sq'  'l_school_i_sq' 'l_school_i_l_margin' 'l_diff_PRI_PAN_l_margin' 'l_diff_PRI_PAN_l_school_i' 'l_lpop_l_margin' 'l_lpop_l_school_i' 'l_diff_PRI_PAN_l_lpop' 'l_lpop_l_mesas' 'l_school_i_l_mesas' 'l_diff_PRI_PAN_l_mesas' 'l_mesas_l_margin' 'l_mesas_l_d_RC_others' 'l_diff_PRI_PAN_l_d_RC_others' 'l_school_i_l_d_RC_others' 'l_d_RC_others_l_lpop' 'ldist_pri' 'ldist_pri_sq' 'ldist_pan' 'ldist_pan_sq'};


%Extended2

% varsx={'l_d_RC_others' 'l_margin' 'lcasillas' 'school_ic' 'l_diff_PRI_PAN' 'lpop' 'l_turnout' 'local' 'ldist_city' ...
% 'l_d_RC_others_sq' 'l_margin_sq' 'lcasillas_sq' 'school_ic_sq' 'l_diff_PRI_PAN_sq' 'lpop_sq' 'l_turnout_sq' 'ldist_city_sq' ...
% 'l_d_RC_others_l_margin' 'l_d_RC_others_lcasillas' 'l_d_RC_others_school_ic' 'l_d_RC_others_l_diff_PRI_PAN' 'l_d_RC_others_lpop' 'l_d_RC_others_l_turnout' 'l_d_RC_others_local' 'l_d_RC_others_ldist_city' ...
% 'l_margin_lcasillas' 'l_margin_school_ic' 'l_margin_l_diff_PRI_PAN' 'l_margin_lpop' 'l_margin_l_turnout' 'l_margin_local' 'l_margin_ldist_city' ...
% 'lcasillas_school_ic' 'lcasillas_l_diff_PRI_PAN' 'lcasillas_lpop' 'lcasillas_l_turnout' 'lcasillas_local' 'lcasillas_ldist_city' ...
% 'school_ic_l_diff_PRI_PAN' 'school_ic_lpop' 'school_ic_l_turnout' 'school_ic_local' 'school_ic_ldist_city' ...
% 'l_diff_PRI_PAN_lpop' 'l_diff_PRI_PAN_l_turnout' 'l_diff_PRI_PAN_local' 'l_diff_PRI_PAN_ldist_city' ...
% 'lpop_l_turnout' 'lpop_local' 'lpop_ldist_city' ...
% 'l_turnout_local' 'l_turnout_ldist_city' ...
% 'local_ldist_city' 'ldist_pri' 'gov_PRI' 'ldist_pan' 'gov_PAN'};


[phats,~]=first_stage(Y,X);

%Expanded specification2
Z_PAN=X(:,end-1:end);
Z_PRI=X(:,end-3:end-2);
X=X(:,1:9);

%H and then M
p_PRI=phats{1,1}(:,1:2); 
p_PAN=phats{1,2}(:,1:2);

X_PRI=[X Z_PRI p_PAN ones(size(X,1),1)];
X_PAN=[X Z_PAN p_PRI ones(size(X,1),1)];

[p_PAN,p_PRI]=predicted_probs(beta,X_PRI,X_PAN);

mean_X=mean(X,1);
mean_X(:,1)=1;
mean_X(:,8)=1;

mean_Z_PAN=mean(Z_PAN,1);
mean_Z_PRI=mean(Z_PRI,1);
mean_Z_PAN(:,2)=0;
mean_Z_PRI(:,2)=0;

mean_p_PAN=mean(p_PAN,1);
mean_p_PRI=mean(p_PRI,1);

if var==12
    mean_p_PAN=[mean_p_PAN(:,3) mean_p_PAN(:,2)];
    mean_p_PRI=[mean_p_PRI(:,3) mean_p_PRI(:,2)];
else
    mean_p_PAN=[0.6 mean_p_PAN(:,2)];
    mean_p_PRI=[0.6 mean_p_PRI(:,2)];
end

mean_X_PRI=[mean_X mean_Z_PRI mean_p_PAN 1];
mean_X_PAN=[mean_X mean_Z_PAN mean_p_PRI 1];
mean_X_PRI=kron(mean_X_PRI,ones(length(range_var),1));
mean_X_PAN=kron(mean_X_PAN,ones(length(range_var),1));

if pri_pan==1
    mean_X_PRI(:,var)=range_var;
else
    mean_X_PAN(:,var)=range_var;
end
    
end

